A Variational Model for Level-set Based Cell Tracking in Time-lapse Fluorescence Microscopy Images

Quantifying the motion and deformation of large numbers of cells through image sequences obtained with ﬂuorescence microscopy is a recurrent task in many biological studies. Automated segmentation and tracking methods are increasingly needed to be able to analyze the large amounts of image data acquired for such studies. In addition, automated techniques have the possibility to improve sensitivity, objectivity, and reproducibility compared to human observers. Recent efforts in this area have revealed the potential of model evolution methods, notably active contours and level sets, for this purpose. One of the disadvantages of such methods is their sensitivity to parameter settings. In this paper we propose a variational model for level-set based cell tracking which involves less parameters with more intuitive meaning compared to previous approaches. The improved performance is demonstrated with experimental results on real time-lapse ﬂuorescence microscopy image data.